Q: In your work with predictive analytics, what behavior or outcome do your models predict?

A: These models are used to match credit card transactions to augmented merchant data to improve readability of online credit card statements.

Q: How does predictive analytics deliver value for your customers – what is one specific way in which it actively improves operational outcomes?

A: Customers expect their bank to know the merchants where they shop. By expanding that knowledge beyond just the merchant name, we can improve our customers’ brand perception and loyalty. This also lowers operational call center costs, as customers can access more details about where they made a purchase.

Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: We increased our library of known restaurants by over 10% in less than 1 week.

Q: What surprising discovery or insight have you unearthed in your data?

A: We were quite surprised by the vibrancy of the communities of micro-workers. They take real pride in their work, and deliver very high quality output.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.
A: Human judgement is often the best source of labeled data for training and testing complicated models. Collecting such data at scale can be daunting. This talk focuses on building automated data pipelines that integrate manual labeling steps.